closed end fund performance on a daily basis: the discovery of a new anomaly

8/13/2019 CLOSED END FUND PERFORMANCE ON A DAILY BASIS: THE DISCOVERY OF A NEW ANOMALY

1/43

CLOSED END FUND PERFORMANCE ON A DAILY BASIS: THE DISCOVERY OF

A NEW ANOMALY

Abstract

Herein we explore the relationships between the NAVs and market prices of closed end

funds. We find the types of relationships that we expected. The market does react to the

newly released NAV in the expected direction and the market does anticipate the changes

in the NAV as expected. By far the most interesting relationship that we have uncovered,

however, is the serendipitous find that the overnight and intraday returns of closed endfunds are negatively auto correlated. This result is found for both the overall sample and

all of the different sub samples that we tested. Our results are found in both univariate

and multivariate tests. We believe the tendency for prices to move in opposite directions

overnight and intraday is due to how the specialists choose to open their assigned stocks.

This negative autocorrelation between intraday and overnight returns appears to us to be

another example of an anomaly.

This set of findings raises several questions. First, are the specialists properly carryingout their assigned task of stabilizing the prices of their securities on the opening? Or are

they exploiting their monopolistic position to the disadvantage of those public investors

who enter market orders overnight? And are the NYSE specialists particularly inclined to

exploit their positions? Second, does this negative autocorrelation occur in the markets

for other types of securities? Or is it just an artifact of how closed end funds are traded?

Third, is the relationship exploitable? That is, could one utilize the tendency of closed

end fund shares to move over the day in the opposite direction from its prior overnightmove, to devise a profitable trading rule? Or does the next trade after the opening remove

the profit potential? We look forward to seeking further answers.


2/43

CLOSED END FUND PERFORMANCE ON A DAILY BASIS: THE DISCOVERY OF

A NEW ANOMALY

Compared to mutual funds, hedge funds, exchange traded funds and even private equity

funds, closed end funds are a relatively backwater type of investment. In absolute terms,

however, closed end funds manage a substantial amount of money. Moreover, their

unique characteristics make them an interesting investment type to study. And yet, what

work has been done on them has been largely focused on a single issue: their discount.

Literature Review

Closed-end Funds are a structured much like mutual funds. They are a collective

investment vehicle but unlike an open end investment company, they have a fixed

number of shares. Once formed, additional shares are rarely issued. The closed-end fund

will hold an initial public offering (IPO) in which the fund management company will

raise the capital to start the fund. After the IPO, the shares trade on a secondary market.

The potential investor can typically purchase these shares from a current holder whowants to sell. The price of a share is determined by supply and demand which is in turn

largely determined by the valuation of the fund's investment portfolio with an adjustment

in the form of a premium/discount set in the market. When the share price is lower than

the NAV, the fund is said to sell at a discount. When the market price is higher than the

NAV, the fund is said to sell at a premium The unknown issue that many researchers

have attempted to understand is why the fund's share price typically sells at a discount to

the fund's net asset value or NAV (the total value of all the securities in the fund dividedby the number of shares in the fund).

Some researchers have explored whether the fund's discount is a result of overestimated

NAVs or biased NAVs. If the fund's NAV is overestimated, the market price is very

unlikely to match or beat that of the NAV (9). Other hypotheses have looked into the

relationship between closed-end funds and their management. Agency costs could impact

the discount (when management cannot perform effectively or has unjustifiably high

fees). Also, the relationship between managerial stock ownership and the fund's discountsor premium - the greater the stock ownership the greater the discount found (2, 3, 9, 17,

20). The exchange on which the fund trades has also been considered. Funds traded on

the New York Stock Exchange tend to show a higher persistence of strong NAV and

market price performance (4). Researchers have found positive relationships between

closed-end fund premiums and discounts and future share prices. Premiums seem to


3/43

international investor sentiment and how they affect fund discounts. Also, the impact of

"noise" traders is examined. It is thought to be a cause of why many CEF trade at

discount. Noise traders enter into trades with little of no specified knowledge of thefactors that impact the values of the traded securities (6, 8, 11, 14, 16, 20, and 21)

Some scholars are interested in the mean-reversion discount issue. They utilize co

integration procedures, which call for examining bond and equity closed-end funds that

"exhibit stationary time-series properties and find statistically significant error correction

terms that quantify the speed of mean reversion. The results from this observation show

that mean reversion" (13, page1) is caused by changes in both market price and NAV" (1,

12, and 13). Some studies explore efforts to exploit risk arbitrage as contributing to fundmis-pricing or the elimination thereof (14, 19).

Other researchers have analyzed the relationship between closed-end fund pricing, and

liquidity and liquidity risk. The two main hypothesizes tested are the closed-end fund

discounts are related to liquidity differences between the closed-end fund and its

underlying portfolio, and the closed-end fund discounts are related to differences in

liquidity risk between closed-end funds and closed-end fund portfolios (7, 18).

Another study involves a model relating how investors differ in their abilities to accessand process relevant information about the fund that they would like to own. According

to this model the fund's discount or premium depends significantly on the quality of

private information about the fund (15).

Even with all of these different styles of research, no individual researcher or team have

come up with a definitive conclusion or consensus that will answer the big question -

Why do a majority of closed-end funds sell in the secondary market at a discount to theirNAV? An analysis of daily closed end returns in the context of their daily NAVs has not

previously been done. That is the focus of our study.

Our Study

Regardless of whether they trade at a small or large discount or even at a premium to

their NAVs, the best long run predictor of a closed end funds price movements is likely

to be the movements in its NAV. As Exhibit 1 illustrates, the NAVs and market prices of

closed end funds tend to move together over time, at least on an aggregate basis. But we

would also like to know how the movements in the NAV and market price of individual


4/43

positions in funds with large discounts in order to try to force them to liquidate or become

open end mutual funds. The larger the discount the greater the profit potential and

therefore the more attractive is the target. Such activities can be expected to keep a fundsmarket price from straying too far away from its NAV.

A Closed end funds NAV is easy to calculate as long as one knows its portfolio

composition. One simply adds up the market values of the portfolio components and

divides by the (generally fixed) number of shares that the fund has outstanding. Most

funds calculate and release their NAV number at the end of each trading day.

Thus with closed end funds we can observe two meaningful valuation numbers on a daily

basis. One, the market price, reflects how the market values the fund as a going concern.

The other, the funds NAV, is a measure of what the fund is worth in liquidation.

Accordingly, a study of how the movements in the NAV impact the market price and vice

versa should generate some interesting findings about the way markets work, especially

how the markets for closed end funds work. Specifically, we are interested in how daily

changes in the NAV impact the market price on a daily basis and how daily changes in

the market price anticipate changes in the NAV.

The NAVs of most funds are released at the end of each trading day. That is, once the

relevant markets have closed, an NAV can and usually will be calculated based on the

closing prices of the funds holdings. During the trading day, however, the funds own

shares will trade at prices which can only anticipate what is happening to its NAV. Once

the end of the day NAV is announced, the markets will have an opportunity to reset the

price at the opening of the following day.

In terms of a time line, the shares of a closed end fund will trade during day t with the

NAV of the previous days close known. The market will also have some knowledge of

the composition of the funds portfolio and knowledge of how the prices of that funds

underling assets are trading during the day. So for example a closed end fund which holds

a portfolio of REIT stocks is likely to move up and down during the day in line with the

market performance of REIT shares. In general the market will be reacting to what it

thinks is happening to the securities which make up each funds portfolio. Marketparticipants will, however, only be able to anticipate changes in a funds NAV with some

uncertainty. The market will, for example, not know how the funds portfolio has

changed since the last portfolio composition report which typically occurs once a quarter.

So inevitably the market will have some uncertainty about what the funds NAV will be

when it is released. Thus the release of the new NAV will contain useful new


5/43

release of the previous days NAV. Thereafter in day t + 1 the funds market price should

fluctuate in light of the markets view of the likely change in the funds NAV to be

reported prior to the start of trading for the following day. So we would expect the NAVrelease to impact the price at which the closed end funds shares open. The movements

during the day should, on the other hand, tend to reflect the markets anticipated change

in the NAV over the course of the day. We explore these relationships herein.

Data

Our data are collected from multiple sources. Most work on daily prices and returnsutilizes what we call close to close returns (CC). Such returns are calculated by collecting

closing prices for each day and computing returns from those values. Typically one

subtracts the t-1 closing price from the closing price for day t and divides by the t-1 close.

For our work on closed end fund returns, however, the close to close return needs to be

viewed as divided into two parts: the overnight return (ON) which we define as the

opening price for period t minus the t-1 closing price divided by the t-1 closing price and

the intra day return (ID) which we define as the day t close minus the day t open divided

by the day t open. Thus the close to close return reflects the impact of both the overnightreturn and the intraday return. All of these return calculations need to be adjusted for the

impact of dividends.

While the daily market prices for closed end funds are available on many data sets, daily

NAVs are more difficult to obtain. Most funds calculate and release their NAVs daily but

most standard data sets do not collect the numbers. After some extensive searching, we

found one data source, Yahoo Finance that maintains daily NAVs on a large number of

closed end funds. Most of the Yahoo NAV data series started rather recently and many of

funds do not report pre-2000 data. In order to match up NAV data with other price data

and include as many funds in our analysis as possible, our analysis period starts from the

first trading day of year 2000 and ends on June 20, 2006. As a result, we have been able

to assemble a data set consisting of 484 closed end funds. About half of the funds have

daily data for the entire period while others have data covering a lesser time frame.

Exhibit 2 groups funds by the number of trading days for which we have data. Our data

set consists of the daily open, close and NAV for each fund for each trading day. We alsoadd the corresponding market returns for each day. With these basic data we have

calculated returns and lagged returns for close to close, overnight and intra day periods.

We also computed returns and lagged returns for the NAVs and a market index.

We use S&P 500 as a proxy for the market and its daily price and return data are also


6/43

Hypotheses

We expect to find the following relationships:

NAV

We expect that the NAV will be anticipated by the market such that the NAV return will

be a function of the intraday return of the day at the end of which the NAV will beannounced.

Overnight Return

We expect that the overnight return will react to the NAV return which becomes

available at the end of the previous days trading.

Intraday Return

We expect that the intraday return will reflect an attempt on the markets part to

anticipate the NAV return.

Univariate Results

Because we are working with daily data on a large number of funds, we have a huge

number of observations to work with consisting of around 50,000 data points. Exhibit 3

contains means, standard deviations and correlations for our set of variables.

Looking first at the means we see that the mean of the overnight returns (0.031) is much

larger than that of the intraday returns (0.009). Moreover, the overnight returns have an

average value that is almost identical to that of the NAVs (0.0314 vs. 0.0320). And yet

the intraday returns have the higher standard deviations (0.95 vs. 0.56). Viewed in

isolation this result suggests that the market tends to react to the overnight news,especially the release of the new NAV at the opening, thereby establishing the start price

from which most fluctuations throughout the day tend to balance out. Relatively little

further directional movement is observed on balance. Individual funds may see their

prices move during the day but when those movements are averaged over funds and over

time, the net price change during the day is much smaller than the net change overnight.


7/43

sample of funds had traded largely at a discount. But at certain times the average value of

P/NAV was greater than one. We can also see a slight upward trend in the ratio consistent

with a decline in the average value of the discount.

Turning to the correlations, we observe that almost everything is significantly correlated

with everything else. None the less some correlations are much higher than others. Lets

start with the NAV return. Note that the NAV return is the result of subtracting the NAV

of day t 1 from the NAV of day t where the NAV of day t is the NAV released at the

end of day t but only available to trade on at the beginning of day t +1 . Thus the NAV

return relates to the results of the activity over day t . As we would expect, the NAV

return is positively correlated with the overnight return and the lagged intraday return. In

other words the intraday return for day t 1 and the overnight return for day t. curiously

we also find a positive correlation between the NAV return and the lagged NAV return,

suggesting some degree of momentum in the NAVs. Such apparent momentum may be

due to lags in the repricing of certain portfolio components. If for example a security does

not trade in a particular day, its last reported price will be stale. And yet that will be the

last price. If it trades the next day, the update will be reflected in the next NAV report. To

the extent that a funds shares move together, the catching up of stale price quotes couldproduce what looks like momentum.

We use the S&P return to proxy for the market. We observe that the NAV, close to close

and intra day returns are all relatively highly correlated with the market (0.099, 0.089,

and 0.071, respectively). And yet the overnight return is essentially uncorrelated with

either the S&P return (-0.0044) or the lagged S&P return (0.0004). This is the first hint

that something curious may be happening at the opening.

Now looking at the overnight return correlations, the number that jumps out at us is the

very high negative correlation with the intraday return (-0.52). We also see a relatively

high negative correlation with the lagged intraday return (-0.13). Clearly these numbers

suggest some negative autocorrelation in the intraday and overnight returns. If the market

price is up during the day it tends to be down overnight and then up for the following

day. Similarly if it is down during the day, it tends to be up overnight and then down

again over the following day. We even find a positive correlation with the laggedovernight return (0.10). This finding is also in line with a negative autocorrelation story.

That is the overnight returns tend to be in the opposite direction from the intraday returns.

We were not looking for and did not expect to find this result. But upon reflection, we

think that this high level of negative autocorrelation relates to the microstructure of


8/43

the close should be a rather reliable index of the markets view of the securitys end of

the day worth.

Once the market closes some of the unfilled limit orders will expire (day orders). Only

those unfilled good till cancelled limit orders will remain. Moreover, if the specialist was

using his or her own position to supply the bid or ask on one or both sides of the quote,

that part of the quote will also disappear at days end. The specialist is under no

obligation to reenter, at the opening, his or her quotes at those end of day levels. As a

result of the disappearance of much of the previous days unfilled orders, the width

between the bid price (highest unfilled buy offer from the prior day) and ask price (lowest

unfilled sell offer from the prior day) appearing on the specialists books is very likely to

widen. The specialist will, at a minimum, have considerable flexibility to set the opening

somewhere between the highest unfilled bid and the lowest unfilled offer. And if the size

of these orders to buy or to sell is small relative to the size of the overnight order flow,

the specialist would be able to set the opening outside of this range.

For an actively traded security, orders both to buy and sell the security are likely to have

come into the market prior to the opening. The specialist will arrive at the exchange andsee the unfilled and un canceled limit orders from the prior day plus the new orders which

have come in overnight. Like the vast majority orders generally, most of the orders which

have come in overnight will be market orders (as opposed to limit, short or stop orders).

All such market orders must be executed at the opening. The overnight pile may also

contain a few limit orders. These overnight limit orders will, however, only need to be

filled as part of the opening transaction if the opening price is at a level which requires

their filling. For example a limit order must be part of the opening trade if it is to sell at a

level below the opening or to buy at above the opening price. That is the specialist can

not trade through an open limit order and leave it unfilled. Typically, as the trading day is

about to begin, the specialist will face an imbalance of orders. That is, the incoming

market orders to buy will be for a greater number of shares than the corresponding sell

orders or the new sell at market orders will be for a greater number of shares than the

corresponding buy orders. But recall that all the market orders must go off at the opening

price. What is the specialist likely to do?

The specialist could simply open the security at the previous close and make up whatever

the imbalance is out of inventory. So if the market orders to buy outnumber those to sell,

the specialist would sell the difference out of his or her own inventory and if the sells

exceed the buys, the specialist would purchase the excess and add them to inventory.

That approach would amount to leaning against the wind. Such a strategy might be


9/43

exceeded by) sell orders. A savvy specialist would not want to ignore the information

reflected in the new NAV in deciding at what new price level to open his or her fund.

Accordingly an alternative strategy for the specialist would be to move the opening price

away from the previous close in the direction of the order imbalance which is probably

also in the direction of the change in the NAV. Thus if the imbalance was in the direction

of an excess of sell (buy) orders, the specialist would tend to open the security below

(above) the previous close. That way the specialist might be able to trigger enough limit

orders below (above) the prior close to offset the imbalance. And if the specialists had to

fill some of the orders by buying into (selling out of) inventory, the purchases (sales)

would at least be at a price below (above) the prior close. The specialist would reap at

least two advantages from this approach. First, this strategy would tend to trigger more

limit order execution than a strategy of filling the imbalance gap out of inventory. The

specialist receives a fee for exercising limit orders. The more limit orders that are

exercised, the greater the fees thereby earned. Second, by using limit orders to cover part

or the entire shortfall the specialist limits or avoids changes in his or her own inventory.

Assuming that the specialist already has his or her inventory position at the preferred

level, not having to change it at the opening is advantageous. Specialists are likely tohave a target inventory for each security that they manage. For example they may want to

hold an inventory equal to X% of the average daily trading volume of the securities that

they handle. They probably try to end each day close to that target so that when they go

home at night, they are not unduly at risk. Of course sometimes the specialist may end the

prior day away from the target inventory level and therefore wish to adjust his or her

inventory at the days beginning. On such occasions, the specialist may take advantage of

the imbalance to make the adjustment, but only if the imbalance is in the desired

direction.

A third possible advantage of moving the price away from its prior level is that additional

trading in the specialists assigned securities is thereby encouraged. That is, if the

opening is lower than the prior close, buyers may be induced to come into the market and

trade. Similarly by opening at above the prior close, sellers are encouraged to enter. Thus

those would be traders who wait until after the market has opened to enter their orders,

may be more stimulated to trade if the opening is away from the prior close than if it is atthe prior close. After all, they probably could have traded yesterday at the prior close

level. A greater level of trading activity is to the specialists advantage as he or she can

earn both a spread and a limit order execution fee on a percentage of the trades. The more

trading activity, the more opportunity for the specialist to make money on the trades.


10/43

extent that the market has anticipated that information, the closing price will already have

that impact baked into the price at close. So the degree to which the overnight return

reflects new information embedded in the new NAV report and the extent that it reflectsthe specialist setting the opening to his or her own benefit is an empirical question. Our

univariate results show a strongly negative autocorrelation in overnight and intraday

market returns. The intraday returns and overnight returns tend to switch signs with

considerable regularity. These autocorrelations are consistent with our story about the

specialists behavior.

Two bits of real world evidence tend to bolster our interpretation of how the market

microstructure can impact pricing. First, we cite the SEC action in obtaining convictions

of specialists. Specifically, two Van der Moolen Specialists were convicted of fraud in

the first of a number of cases involving specialists. The two had been charged with front

running and inter positioning. Front running involves trading ahead of a large order in

an attempt to take advantage of the old price structure before the large order causes it to

change. Inter positioning refers to the practice of buying from one public trader and

selling to another, thereby taking a profit out of the trade when the two traders could have

been matched up directly with each other for a better price for both parties to thetransaction ( Bloomberg, Ex-Van der Moolen Specialists Convicted of Fraud,

BLOOMBERG, July 15, 2006).

The second case involves another SEC action. In this matter quoting from the SEC

release a trader named Thomas E. Edgar was charge with a manipulative trading

practice known as "marking-the-close". Quoting from the SEC release:

The Commission's Complaint alleges that one way Edgar carried out his scheme was

to mark-the-close in an attempt to increase his profit from the sale of closed-end

funds that he owned. Specifically, when Edgar owned a large number of shares of a

closed-end fund, typically 2,000 shares, he often placed an additional market orderto buy approximately 100 or 200 shares of the same closed-end fund within a fewminutes of the close of the market. The execution of these additional market buy

orders resulted in an increase to the closing price of the fund. The Commission's

Complaint alleges that Edgar's purpose in placing the additional market buy orders atthe end of one trading day was to cause an increase to the price of the fund and then

to profit from the higher sale price at the beginning of the next trading day(23).

Neither of these cases is exactly on point with our findings. The first, however illustrates

how specialists may misuse their positions to mange the market to their advantage and to

th di d t f th t di bli Th d ill t t h th k t f l d


11/43

funds contains domestic funds, international funds, bond funds, stock funds, funds listed

on the NYSE and funds listed on the AMEX. Does any subgroup of funds dominate our

results? Or do our results hold up across fund types? We approach that question bylooking at separate statistics for subgroups of funds.

Because many closed end funds are international we first decomposed our sample into

three categories: 1. Domestic whose portfolios were 100% domestic securities, 2. Mixed,

whose portfolios contained at least 50% domestic securities, and 3. Foreign, whose

portfolios contained more than 50% foreign securities. Not surprisingly the statistics for

the domestic group were very similar to that of the entire sample. The domestics made up

about three fourths of the total. Still the other two grouping also had similar statistics.

Exhibit 6 provides a graphic picture of the percentage distributions of foreign holdings.

Panel B of Exhibit 3 contains univariate statistics for the first three sub samples.

Comparing the three groups we see that the domestic and mixed groups have much in

common. For both the overnight return has a much higher mean than the intraday return

(0.032 and 0.029 verses 0.0057 and 0.0061 for domestic and mixed respectively). The

foreign group, in contrast has a higher intraday mean return relative to its overnightreturn (0.035 verses 0.031). Perhaps the fact that most foreign securities trade primarily

in different time zones from the U. S. markets accounts for the difference. Also of interest

is that the S&P return is more highly correlated with the NAV returns for the mixed and

foreign funds than for the domestic (0.064 for domestic compared to 0.201 for mixed and

0.148 for foreign). Still our most interesting finding holds for all three groups. The

negative correlation between the intraday and overnight returns are - 0.522, - 0.576 and

- 0.433 for domestic, mixed and foreign respectively. We also observe the same sign

alternation for the correlations of the lagged values for the intraday and overnight returns.

So whatever is causing the negative autocorrelation seems to be happening to all three

subgroups of funds.

We also preformed two other decompositions on our sample. First we subdivided our

sample between stocks, balanced and bond funds. Those with an equity component of

80% or more were classified as stock funds. Those with 80% or more of debt securities in

their portfolios were classified as bond funds. Those with at least 20% of both stock andbond holdings were classified as balanced. About three fourths of our sample were bond

funds with most of the rest falling into the stock fund group. For all three subgroups, the

mean of the overnight returns were considerably above the mean intraday returns (Panel

C). The differences were, however, much greater for the bond funds than for the other

two groups. Once again the negative autocorrelations showed up strongly in each sub


12/43

of the total. Panel C contains the univariate statistics. Looking at the overnight and

intraday return means again we see that the overnight returns have by far the larger

means. But for the AMEX the differences are much greater (0.042 verses 0.0005) thanthe NYSE (0.027 verses 0.013). Also both exchange listings have a high negative

correlation between the overnight and intraday returns but the AMEX correlation is

appreciably smaller (- 0.377) than the NYSE (- 0.541). As with the total sample, the lag

variable correlations also alternate. So the negative autocorrelations do not appear to be

restricted to a particular exchange.

Overall we find that our negative autocorrelations in the overnight and intraday returns

are quite consistent for the various subgroups that we tested. The same patterns are

observed for domestic, mixed, foreign, NYSE, AMEX, stock, balanced and bond funds.

On a univariate basis at least, overnight and intraday returns tend to move in opposite

directions.

Multivariate Analysis

First we consider the determinants of the NAV returns. Our correlation analysis suggests

that we regress the NAV return on five variables: 1. the S&P return, 2. the overnight

return, 3. the lagged NAV return, 4. the lagged overnight return and 5. the lagged

intraday return. We find (Exhibit 6) a positive and significant coefficient for each

variable. Thus we see that the NAV return is positively related to the returns in the

market and as well as various other returns for the funds shares. And we also find a

significant relation with the lagged NAV return, suggesting some degree of apparent

momentum in NAV returns. In other words we find that the univariate correlations hold

up in a multivariate context. In particular we see a significant relationship between the

NAV return and the overnight return. Thus the market appears to be reacting to the new

NAV report as expected. A rise in the NAV tends to be associated with a rise in themarket price from the prior close to the next days opening. In addition the prior days

intra day return is positively associated with the NAV return. The market does seem to be

trying to anticipate changes in the NAV. So far so good. The regressions R squared is,

however, a rather low .021. Thus about 98% of the variability of the NAV return is

unexplained by the variables of our model. Clearly our model is not capturing the bulk of


13/43


14/43

This differential result by exchange coupled with a similar univariate result suggests that

the specialists on the AMEX may behave somewhat differently from those on the NYSE.

These various sets of results evidence an unusually large amount of explanatory power

for a model explaining daily returns. Unfortunately, one could not use the model for

trading purposes because the key component, intraday return is an ex post variable.

Since, however, the overnight and intraday returns are highly correlated; one could use

knowledge of the overnight returns to forecast the corresponding intraday returns.

Accordingly we regress the intraday return on three two variables which are available ex

anti: 1. the overnight return, 2. the lagged NAV return (Exhibit 9). Both variables are

highly significant with the former variable by far the more powerful. Thus we find that

the intraday return tends to be positively associated with the lagged NAV return and

negatively associated with the overnight return. The R squared for this relationship is

0.28 indicating that our model is able to explain about 28% of the intraday return

variability in closed end fund returns with this simple model. Again we find consistent

results for the sub samples as shown in panels B, C, and D of Exhibit 9. No matter how

we divide the sample up, each sub sample shows a substantial amount of negativeautocorrelation. This result appears to be inconsistent with the weak form of market

efficiency and thus is potentially an anomaly.

Conclusion

We set out to explore the relationships between the NAVs and market prices of closed

end funds. We found the types of relationships that we expected. The market does react

to the newly released NAV in the expected direction and the market does anticipate the

changes in the NAV as expected. By far the most interesting relationship that we have

uncovered in this bit of research, however, is a serendipitous find. We were not looking

for it and did not expect it. But what we found was that the overnight and intraday returns

of closed end funds are negatively auto correlated. Overnight and intraday returns tend

not only to move inversely with each other but do so quite strongly. This result is found

not only for the overall sample but for all of the different sub samples that we tested. We

believe this tendency for prices to move in opposite directions overnight and intraday isdue to how the specialists choose to open their assigned stocks. This negative

autocorrelation between intraday and overnight returns appears to us to be another

example of an anomaly.

This set of findings raises several questions. First, are the specialists properly carrying


15/43

its prior overnight move, to devise a profitable trading rule? Or does the next trade after

the opening remove the profit potential? We look forward to seeking further answers.

References

1. Arora, et al 2002, "Closed-End Funds: A Dynamic Model of Premiums andDiscounts".

2. Barclay, "Private benefits from block ownership and discounts on closed-endfunds",Journal of Financial Economics, Vol.33, Iss.3 (June 1993), pp.263-291.

3. Barone-Adesi and Kim 1999, "Incomplete information and the closed-end funddiscount".

4. Bers and Madura, "Why does performance persistence vary among closed-endfunds?Journal of Financial Services Research, Vol.17, No.2 (Aug 2000),

pp.127-147.

5. Chay and Trzcinka, "Managerial performance and the cross-sectional pricing ofclosed-end funds".

6. Chen, Kan and Miller, "Are the discounts on closed-end funds a sentimentindex? The Journal of Finance, Vol.48, No.2 (June 1993), pp.795-800.

7. Cherkes, et al 2005, "Liquidity and Closed-End Funds".8. De Long and Shleifer, "Closed-End Fund Discounts",Journal of Portfolio

Management Vol 18 No 2 (Winter 1992; 18) pp 46-53


16/43

11.Garay 2000, "The Closed-End Domestic Fund and Closed-End Country FundDiscount Puzzles: A Review of the Literature".

12.Gasbarro, Johnson and Zumwalt, "Evidence on the mean-reverting tendencies ofclosed-end fund discounts", The Financial Review, Vol.38, No.2 (May 2003),

pp.273-291.

13.Gasbarro and Zumwalt, year? "Time-Varying Characteristics of Closed-End FundDiscounts".

14.Gemmill and Thomas 2000, "Sentiment, Expenses and Arbitrage in Explainingthe Discount on Closed-End Funds".

15.Grullon and Wang, "Closed-End Fund Discounts with Informed OwnershipDifferential",Journal of Financial Intermediation, Vol.10(2001), pp.171205.

16.Lee, Shlerfer and Thaler, "Investor Sentiment and the Closed-end Fund Puzzle",The Journal of Finance, Vol.XLVI, No.1 (March 1991), pp.75-109.

17.Malkiel, "The structure of closed-end fund discounts revisited",Journal ofPortfolio Management, Vol.21, No.4 (Summer 1995), pp.32-38.

18.Manzler 2005, "Liquidity, Liquidity Risk and the Closed-End Fund Discount".19.Pontiff, "Costly Arbitrage: Evidence from closed-end funds", The Quarterly

Journal of Economics, Vol.111, No.4 (Nov. 1996), pp.1135-1151.

20.Richard and Wiggins, "The information content of closed-end country fund


17/43

Exhibit 1A: Average Daily NAV and Market Price- All Funds (484 funds)

6

8

10

12

14

16

18

20

1/3/00 1/3/01 1/3/02 1/3/03 1/3/04 1/3/05 1/3/06

Date

Pri

ce

Level

NAV

Market

Price


18/43

Exhibit 1B: Average Daily NAV and Market Price by Percentage Invested Globally

Domestic Funds (351 funds)

6

8

10

12

14

16

18

20

1/3/00

1/3/01

1/3/02

1/3/03

1/3/04

1/3/05

1/3/06

Date

Price

Level

NAV

Market Price

Balanced Funds (85 funds)

6

8

10

12

14

16

18

20

1/3/00

1/3/01

1/3/02

1/3/03

1/3/04

1/3/05

1/3/06

Date

Price

Level

NAV

Market Price

Global Funds (43 Funds)

6

8

10

12

14

16

18

20

1/3/00

1/3/01

1/3/02

1/3/03

1/3/04

1/3/05

1/3/06

Date

Price

Level

NAV

Market Price


19/43

Exhibit 1C: Average Daily NAV and Market Price by Portfol io Composi tion

Stock Funds (144 funds)

4

6

8

10

12

14

16

18

20

22

1/3/00

1/3/01

1/3/02

1/3/03

1/3/04

1/3/05

1/3/06

Date

Price

Level

NAV

Market Price


4

6

8

10

12

14

16

18

20

22

1/3/00

1/3/01

1/3/02

1/3/03

1/3/04

1/3/05

1/3/06

Date

Price

Level

NAV

Market Price

Bond Funds (307 funds)

4

6

8

10

12

14

16

18

20

22

1/3/00

1/3/01

1/3/02

1/3/03

1/3/04

1/3/05

1/3/06

Date

Price

Level

NAV

Market Price


20/43

Exhibit 1D: Average Daily NAV and Market Price by Exchange

AMEX (106 funds)

67

8

9

10

11

12

13

14

15

16

1/3/00

1/3/01

1/3/02

1/3/03

1/3/04

1/3/05

1/3/06

Date

Price

Level

NAV

Market Price

NYSE

67

8

9

10

11

12

13

14

15

16

1/3/00

1/3/01

1/3/02

1/3/03

1/3/04

1/3/05

1/3/06

Date

Price

Level

NAV

Market Price


21/43

Exhibit 2A: Trading Day Frequency Distribution

- All Funds (484 funds)

4

1624 25

6

32

21

62

46

33

219

0

50

100

150

200

250

--> 100 100 - 200 200 - 300 300 - 400 400 - 500 500 - 600 600 - 700 700 -

800

800 - 900 900 -

1000

1000 -->

# of trading days

#o

ffunds


22/43

Exhibit 2B: Trading Day Frequency Distribution by Percentage Invested Globally

Domestic Funds

09 13 13

316 11

55

41

22

168

0

20

40

60

80

100

120

140

160

180

-->

100

100 -

200

200 -

300

300 -

400

400 -

500

500 -

600

600 -

700

700

- 800

800 -

900

900 -

1000

1000

-->

# of Trading Days (mean=1142.79)

#

ofFunds


02

97

3

7 76

5 5

34

0

5

10

15

20

25

30

35

40

-->

100

100 -

200

200 -

300

300 -

400

400 -

500

500 -

600

600 -

700

700 -

800

800 -

900

900 -

1000

1000

-->

3 of Trading Days (mean=1019.21)

#

ofFunds

Global Funds (43 funds)

1

5

2

4

0

7

10 0

6

17

0

2

4

6

8

10

12

14

16

18

-->

100

100 -

200

200 -

300

300 -

400

400 -

500

500 -

600

600 -

700

700 -

800

800 -

900

900 -

1000

1000

-->


#

ofFunds


23/43

Exhibit 2C: Trading Day Frequency Distribution by Port folio Composition

Stock Funds (144 Funds)

0

11

20

23

6

22

10 10

24

36

0

5

10

15

20

25

30

35

40

-->

100

100 -

200

200 -

300

300 -

400

400 -

500

500 -

600

600 -

700

700 -

800

800 -

900

900 -

1000

1000

-->


#

ofFunds


0 01

0 0

5

1

5

3

2

10

0

2

4

6

8

10

12

-->

100

100 -

200

200 -

300

300 -

400

400 -

500

500 -

600

600 -

700

700 -

800

800 -

900

900 -

1000

1000

-->


#

ofFunds


0 0 3 2 05 9

47

4127

173

0

20

40

60

80

100

120

140

160

180

200

-->

100

100 -

200

200 -

300

300 -

400

400 -

500

500 -

600

600 -

700

700

- 800

800 -

900

900 -

1000

1000

-->


#

ofFunds


24/43

Exhibit 2D: Trading Day Frequency Distribution by Exchange

NYSE (372 funds)

011 20 22

4

2515

50

17 16

192

0

50

100

150

200

250

-->

100

100 -

200

200 -

300

300 -

400

400 -

500

500 -

600

600 -

700

700

- 800

800 -

900

900 -

1000

1000

-->


#

ofFunds

AMEX (106 funds)

0 0

4

3 2

75

12

29

17

27

0

5

10

15

20

25

30

35

-->

100

100 -

200

200 -

300

300 -

400

400 -

500

500 -

600

600 -

700

700 -

800

800 -

900

900 -

1000

1000

-->


#

ofFunds


25/43

Exhibit 3: Correlation Matrix

Panel A: Al l Funds (484 funds)

Variable SPret NAVret Pret_CC Pret_ON Pret_ID lagSPret lagNAVret lagPret_CC lagPret_ON lagPret_ID

Mean 0.007 0.032 0.037 0.031 0.009 0.007 0.032 0.037 0.031 0.009

Std. Dev. 1.195 0.499 0.795 0.565 0.958 1.195 0.499 0.795 0.565 0.958

SPret 1.000 0.099 0.089 -0.004 0.071 -0.045 0.004 0.005 0.000 0.004

NAVret 1.000 -0.073 0.046 -0.073 -0.006 0.047 0.073 -0.019 0.072

Pret_CC 1.000 0.049 0.776 0.027 0.131 -0.042 0.033 -0.053

Pret_ON 1.000 -0.519 0.000 0.048 -0.089 0.100 -0.134

Pret_ID 1.000 0.021 0.081 0.022 -0.032 0.040

lagSPret 1.000 0.099 0.089 -0.004 0.071

lagNAVret 1.000 -0.073 0.046 -0.073

lagPret_CC 1.000 0.049 0.776

lagPret_ON 1.000 -0.519

lagPret_ID 1.000


26/43

Exhibit 3 - continued

Panel B: by Percentage Invested Globally

Variable SPret NAVret Pret_CC Pret_ON Pret_ID lagSPret lagNAVret lagPret_CC lagPret_ON lagPret_ID

Mean 0.006 0.030 0.035 0.032 0.006 0.006 0.030 0.035 0.032 0.006

Std. Dev. 1.089 0.444 0.753 0.536 0.901 1.089 0.444 0.753 0.536 0.901

SPret 1.000 0.064 0.061 -0.001 0.049 -0.033 0.005 0.009 0.002 0.005NAVret 1.000 -0.081 0.036 -0.079 -0.039 0.064 0.071 -0.019 0.069

Pret_CC 1.000 0.063 0.780 0.017 0.128 -0.041 0.030 -0.052

Pret_ON 1.000 -0.522 0.001 0.049 -0.104 0.101 -0.148

Pret_ID 1.000 0.014 0.080 0.029 -0.036 0.047

lagSPret 1.000 0.064 0.061 -0.001 0.049

lagNAVret 1.000 -0.081 0.036 -0.079

lagPret_CC 1.000 0.063 0.780

lagPret_ON 1.000 -0.522

lagPret_ID 1.000Mean 0.006 0.027 0.036 0.029 0.008 0.006 0.027 0.036 0.029 0.008

Std. Dev. 1.060 0.581 0.829 0.601 1.041 1.060 0.581 0.829 0.601 1.041

SPret 1.000 0.201 0.145 -0.027 0.123 -0.031 -0.001 -0.007 -0.006 0.001

NAVret 1.000 -0.121 0.040 -0.099 0.033 -0.008 0.060 -0.021 0.059

Pret_CC 1.000 -0.037 0.781 0.041 0.137 -0.059 0.036 -0.068

Pret_ON 1.000 -0.576 0.003 0.042 -0.080 0.104 -0.125

Pret_ID 1.000 0.030 0.080 0.006 -0.030 0.025

lagSPret 1.000 0.201 0.145 -0.027 0.123

lagNAVret 1.000 -0.121 0.040 -0.099lagPret_CC 1.000 -0.037 0.781

lagPret_ON 1.000 -0.576

lagPret_ID 1.000

Mean 0.000 0.060 0.059 0.031 0.035 -0.001 0.060 0.059 0.031 0.035

Std. Dev. 1.117 0.760 1.052 0.729 1.248 1.117 0.760 1.052 0.729 1.248

SPret 1.000 0.148 0.194 0.006 0.180 -0.027 0.007 -0.006 -0.003 0.001

NAVret 1.000 0.015 0.102 -0.013 0.139 0.053 0.104 -0.014 0.104

Pret_CC 1.000 0.087 0.751 0.075 0.138 -0.025 0.041 -0.039

Pret_ON 1.000 -0.433 0.023 0.056 -0.031 0.085 -0.076

Pret_ID 1.000 0.050 0.086 0.007 -0.019 0.021

lagSPret 1.000 0.148 0.194 0.006 0.180

lagNAVret 1.000 0.015 0.102 -0.013

lagPret_CC 1.000 0.087 0.751

lagPret_ON 1.000 -0.433

lagPret_ID 1.000

Note: Domestic Funds invest 0% globally; Balanced funds 0 - 50%; Global funds > 50%.

Domestic(351funds)

Bala

nced(85funds)

Global(43funds)


27/43


Panel C: by Portfolio Composition

Variable Spret_CC NAVret Pret_CC Pret_ON Pret_ID lagSPret_CC lagNAVret lagPret_CC lagPret_ON lagPret_ID

Mean 0.012 0.045 0.042 0.027 0.018 0.011 0.045 0.042 0.027 0.017

Std. Dev. 0.959 0.781 0.940 0.599 1.109 0.959 0.781 0.940 0.599 1.109

Spret_CC 1.000 0.452 0.274 -0.018 0.249 -0.036 -0.006 -0.012 -0.003 -0.008NAVret 1.000 0.180 0.036 0.144 0.039 0.009 0.049 0.003 0.045

Pret_CC 1.000 0.042 0.803 0.081 0.141 0.000 0.024 -0.010

Pret_ON 1.000 -0.444 0.020 0.038 -0.027 0.075 -0.065

Pret_ID 1.000 0.060 0.102 0.017 -0.020 0.031

lagSPret_CC 1.000 0.452 0.274 -0.018 0.249

lagNAVret 1.000 0.180 0.036 0.144

lagPret_CC 1.000 0.042 0.803

lagPret_ON 1.000 -0.444

lagPret_ID 1.000Mean 0.013 0.038 0.053 0.033 0.022 0.013 0.038 0.053 0.033 0.022

Std. Dev. 1.003 0.588 0.835 0.613 1.045 1.004 0.588 0.835 0.613 1.045

Spret_CC 1.000 0.263 0.130 -0.017 0.105 -0.036 -0.002 -0.007 0.012 -0.007

NAVret 1.000 -0.167 0.057 -0.134 0.036 -0.008 0.078 -0.022 0.068

Pret_CC 1.000 -0.030 0.775 0.054 0.155 -0.045 0.035 -0.055

Pret_ON 1.000 -0.577 0.007 0.030 -0.085 0.104 -0.133

Pret_ID 1.000 0.029 0.103 0.017 -0.032 0.037

lagSPret_CC 1.000 0.263 0.130 -0.017 0.105

lagNAVret 1.000 -0.167 0.057 -0.134lagPret_CC 1.000 -0.030 0.775

lagPret_ON -0.577

lagPret_ID 1.000

Mean 0.010 0.028 0.035 0.030 0.007 0.010 0.028 0.035 0.030 0.007

Std. Dev. 1.064 0.382 0.731 0.532 0.880 1.065 0.382 0.731 0.532 0.880

Spret_CC 1.000 -0.067 0.040 -0.005 0.033 -0.035 0.010 0.012 0.002 0.009

NAVret 1.000 -0.238 0.055 -0.214 -0.030 0.096 0.092 -0.032 0.095

Pret_CC 1.000 0.065 0.772 0.009 0.128 -0.049 0.033 -0.061

Pret_ON 1.000 -0.535 -0.004 0.060 -0.111 0.112 -0.161Pret_ID 1.000 0.010 0.068 0.028 -0.041 0.050

lagSPret_CC 1.000 -0.067 0.040 -0.005 0.033

lagNAVret 1.000 -0.238 0.055 -0.214

lagPret_CC 1.000 0.065 0.772

lagPret_ON 1.000 -0.535

lagPret_ID 1.000

Note: Stock funds invest >80% in equities; bond funds >80% in bonds; balanced funds less than 80% in stocks or bonds.

Stock

(144

funds)

Bala

nced

(27

funds)

Bond

(307

funds)


28/43


Panel D: by Exchange

Variable Spret_CC NAVret Pret_CC Pret_ON Pret_ID lagSPret_CC lagNAVret lagPret_CC lagPret_ON lagPret_ID

Mean 0.023 0.036 0.034 0.042 -0.005 0.024 0.036 0.034 0.042 -0.005

Std. Dev. 0.937 0.461 0.792 0.513 0.857 0.937 0.461 0.792 0.513 0.857

Spret_CC 1.000 0.036 0.055 -0.003 0.054 -0.049 0.006 0.012 0.007 0.006NAVret 1.000 0.015 -0.005 0.021 -0.056 0.101 0.065 -0.008 0.066

Pret_CC 1.000 0.225 0.791 0.022 0.142 -0.049 0.023 -0.060

Pret_ON 1.000 -0.377 0.001 0.061 -0.109 0.068 -0.143

Pret_ID 1.000 0.022 0.097 0.023 -0.019 0.032

lagSPret_CC 1.000 0.036 0.055 -0.003 0.053

lagNAVret 1.000 0.015 -0.005 0.021

lagPret_CC 1.000 0.225 0.791

lagPret_ON 1.000 -0.377

lagPret_ID 1.000Mean 0.007 0.031 0.038 0.027 0.013 0.007 0.031 0.038 0.027 0.013

Std. Dev. 1.063 0.507 0.781 0.559 0.958 1.063 0.507 0.781 0.559 0.958

Spret_CC 1.000 0.110 0.104 -0.009 0.089 -0.033 0.004 0.004 0.000 0.004

NAVret 1.000 -0.094 0.057 -0.091 0.005 0.036 0.075 -0.021 0.074

Pret_CC 1.000 0.015 0.780 0.029 0.129 -0.032 0.033 -0.043

Pret_ON 1.000 -0.541 0.001 0.046 -0.083 0.109 -0.133

Pret_ID 1.000 0.022 0.078 0.025 -0.038 0.046

lagSPret_CC 1.000 0.110 0.104 -0.009 0.089

lagNAVret 1.000 -0.093 0.057 -0.091lagPret_CC 1.000 0.015 0.780

lagPret_ON 1.000 -0.541

lagPret_ID 1.000

AMEX(106funds)

NY

SE(372funds)


29/43

Exhibit 4A: Discount (Premium) Frequency Distribution

- All Funds (484 funds)

7

65

136

144

82

50

0

20

40

60

80

100

120

140

160

--> 0.85 0.85 -- 0.90 0.90 -- 0.95 0.95 -- 1.00 1.00 -- 1.05 1.05 -->

Average Price/NAV ratio

#

ofFunds

Exhibit 4B: Discount (Premium) Frequency Distribution by Percentage Invested Globally


30/43

Exhibit 4B: Discount (Premium) Frequency Distribution by Percentage Invested Globally


1

15

24 24

129

0

5

10

15

20

25

30

--> 0.85 0.85 -

0.90

0.90 -

0.95

0.95 -

1.00

1.00 -

1.05

1.05 -->

Average Price/NAV Ratio (mean=0.9695)

#

ofFunds


3

40

97111

64

36

0

20

40

60

80

100

120

--> 0.85 0.85 -

0.90

0.90 -

0.95

0.95 -

1.00

1.00 -

1.05

1.05 -->


#

ofFunds


3

9

12

8

65

0

2

4

6

8

10

12

14

--> 0.85 0.85 -

0.90

0.90 -

0.95

0.95 -

1.00

1.00 -

1.05

1.05 -->


#

ofFunds

Exhibit 4C: Discount (Premium) Frequency Distribution by Portfolio Composition


31/43

Exhibit 4C: Discount (Premium) Frequency Distribution by Portfolio Composition


7

32

42

30

20

13

05

1015202530354045

--> 0.85 0.85 -

0.90

0.90 -

0.95

0.95 -

1.00

1.00 -

1.05

1.05 -->


#

ofFunds


0

8

5

6

3

5

0

1

23

45

67

89

--> 0.85 0.85 -

0.90

0.90 -

0.95

0.95 -

1.00

1.00 -

1.05

1.05 -->


#

ofFunds


0

25

87

105

58

32

0

20

40

60

80

100

120

--> 0.85 --> 0.90 0.90 -

0.95

0.95 -

1.00

1.00 -

1.05

1.05 -->

Average Price/NAV Ration (mean=0.9771)

#

ofFunds


32/43

Exhibit 4D: Discount (Premium) Frequency Distr ibution by Exchange

NYSE (372 funds)

5

52

118

107

53

37

0

20

40

60

80

100

120

140

--> 0.85 0.85 - 0.90 0.90 - 0.95 0.95 - 1.00 1.00 - 1.05 1.05 -->

Average Price/NAV Rat io (mean=0.963)

#

ofFunds

AMEX (106 funds)

2

13

16

34

28

13

0

5

10

15

20

25

30

35

40

--> 0.85 0.85 - 0.90 0.90 - 0.95 0.95 - 1.00 1.00 - 1.05 1.05 -->


#

ofFunds


33/43

Exhibit 5A: Time Series of Average Fund Discount (Premium)

- All Funds (484 Funds)

0.8

0.85

0.9

0.95

1

1.05

1/3/00 1/3/01 1/3/02 1/3/03 1/3/04 1/3/05 1/3/06

Date

Price/NAV

ratio

Exhibit 5B: Time Series of Average Fund Discount (Premium) by Percentage Invested Globally


34/43

g ( ) y g y


0.7

0.75

0.8

0.85

0.9

0.95

1

1.05

1.1

1/3/00 1/3/01 1/3/02 1/3/03 1/3/04 1/3/05 1/3/06

Date

Price

/NAVRatio(mean=0.9

725)

Balanced Funds (85 Funds)

0.7

0.75

0.8

0.85

0.9

0.95

1

1.05

1.1

1/3/00 1/3/01 1/3/02 1/3/03 1/3/04 1/3/05 1/3/06

Date

Price

/NAVRatio(mean=0.9

743)


0.7

0.75

0.8

0.85

0.9

0.95

1

1.05

1.1

1/3/00 1/3/01 1/3/02 1/3/03 1/3/04 1/3/05 1/3/06

Date

Price/NA

VRatio(mean=0.9

534)

Exhibit 5C: Time Series of Average Fund Discount (Premium) by Portfolio Composit ion


35/43

g ( ) y p


0.7

0.8

0.9

1

1.1

1.2

1.3

1/3/00 1/3/01 1/3/02 1/3/03 1/3/04 1/3/05 1/3/06

Date

Price

/NAVRatio(mean=0.9

624)


0.7

0.8

0.9

1

1.1

1.2

1.3

1/3/00 1/3/01 1/3/02 1/3/03 1/3/04 1/3/05 1/3/06

Date

Price

/NAVRatio(mean=0.9

850)


0.7

0.8

0.9

1

1.1

1.2

1.3

1/3/00

7/3/00

1/3/01

7/3/01

1/3/02

7/3/02

1/3/03

7/3/03

1/3/04

7/3/04

1/3/05

7/3/05

1/3/06

Date

Price/NAVRatio(mean=0.9

723)

Exhibit 5D: Time Series of Average Fund Discount (Premium) by Exchange


36/43

NYSE (372 funds)

0.8

0.85

0.9

0.95

1

1.05

1.1

1/3/00 1/3/01 1/3/02 1/3/03 1/3/04 1/3/05 1/3/06

Date

Price/NAVRatio(mean=0.9

677)

AMEX (106 funds)

0.8

0.85

0.9

0.95

1

1.05

1.1

1/3/00

7/3/00

1/3/01

7/3/01

1/3/02

7/3/02

1/3/03

7/3/03

1/3/04

7/3/04

1/3/05

7/3/05

1/3/06

Date

Pr

ice/NAVRatio(mean=0.9

855)


37/43

Exhibit 6: Funds by Percentage Invested Globally

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 101 201 301 401

# of Funds

%In

vestedGlobally

484354


38/43



39/43

NAVret = + 1*SPret + 2*Pret_ON + 3*lagNAVret + 4*lagPret_ON + 5*lagPret_ID R2


AMEX (106 funds):

0.031 0.016 -0.003 0.101 0.019 0.039 0.016

(20.51) (9.83) (-1.13) (30.88) (6.01) (20.51)

NYSE (372 funds):

0.027 0.051 0.049 0.049 0.025 0.055 0.025

(35.43) (69.99) (34.42) (30.68) (14.70) (55.27)

Note: t-statistics are inside parentheses.

Exhibit 8: Regression Coefficients with Overnight Market Return as the Dependent Variable


40/43

Pret_ON = + 1*SPret + 2*Pret_ID + 3*lagNAVret + 4*lagPret_ON + 5*lagPret_ID R2

Panel A: All Funds (484 funds)

0.03 0.019 -0.322 0.099 0.035 -0.056 0.308

(46.17) 29.50) (-445.50) (73.03) (24.96) (-66.74)


Domestic* (351 funds):

0.029 0.013 -0.319 0.101 0.029 -0.062 0.303

(41.56) (18.41) (-382.12) (60.97) (18.02) (-63.49)Balanced* (85 funds):

0.029 0.036 -0.35 0.094 0.037 -0.054 0.365

(17.88) (21.31) (-203.34) (31.16) (10.34) (-25.46)

Global* (43 funds):

0.036 0.052 -0.307 0.096 0.052 -0.032 0.27

(11.73) (16.43) (-108.73) (22.12) (9.64) (-10.03)

*Domestic Funds invest 0% globally; Balanced funds 0 - 50%; Global funds > 50%.


Stock* (144 funds):

0.03 0.075 -0.267 0.079 0.043 -0.03 0.237

(18.35) (38.84) (-165.15) (35.63) (13.30) (16.77)

Balanced* (27 funds):

0.036 0.035 -0.35 0.097 0.045 -0.045 0.359

(12.44) (11.48) (-116.68) (18.27) (7.34) (-12.33)

Bond* (307 funds):

0.029 0.008 -0.325 0.114 0.034 -0.061 0.31

(39.83) (11.32) (-386.34) (56.66) (21.01) (-60.02)

*Stock funds invest >80% in equities; bond funds >80% in bonds; balanced funds less than 80% in stocks or bonds.



41/43

NAVret = + 1*SPret + 2*Pret_ID + 3*lagNAVret + 4*lagPret_ON + 5*lagPret_ID R2


AMEX (106 funds):

0.035 0.01 -0.233 0.115 0.012 -0.078 0.172

(23.06) (5.70) (-128.03) (34.24) (3.63) (-40.10)

NYSE (372 funds):

0.028 0.022 -0.326 0.098 0.045 -0.048 0.323

(39.90) (31.55) (-416.01) (65.55) (28.36) (-51.48)


Exhibit 9: Regression Coefficients with Intraday Market Return as the Dependent Variable


42/43

Pret_ID = + 1*Pret_ON + 2*lagNAVret R2

Panel A: All Funds (484 funds)

0.031 -0.885 0.216 0.278

(27.56) (-426.97) (93.67)


Domestic* (351 funds):

0.028 -0.884 0.217 0.29

(23.37) (-383.31) (78.40)Balanced* (85 funds):

0.036 -0.995 0.196 0.351

(12.72) (-201.11) (38.07)

Global* (43 funds):

0.06 -0.839 0.193 0.26

(11.69) (-107.18) (26.78)

*Domestic Funds invest 0% globally; Balanced funds 0 - 50%; Global funds > 50%.


Stock* (144 funds):

0.038 -0.851 0.174 0.224

(12.73) (-159.14) (43.53)

Balanced* (27 funds):

0.046 -0.98 0.233 0.35

(9.33) (-116.07) (26.17)

Bond* (307 funds):

0.027 -0.889 0.256 0.297

(23.01) (-388.59) (78.73)

*Stock funds invest >80% in equities; bond funds >80% in bonds; balanced funds less than 80% in stocks or bonds.



43/43

Pret_ID = + 1*Pret_ON + 2*lagNAVret R2


AMEX* (106 funds):

0.014 -0.64 0.229 0.158

(5.50) (-127.71) (40.71)

NYSE* (372 funds):

0.034 -0.938 0.212 0.312

(28.00) (-415.78) (83.78)


closed end fund performance on a daily basis: the discovery of a new anomaly

Documents